8 citations · 25 across the 16 of their papers we have counts for
17 papers
Generative Retrieval for Unsupervised Text-Based Person Search
Mang Ye, Yucheng Ji, Yang Bai +4
Text-based person search (TBPS) aims to retrieve images of a target person from a large image gallery based on a given natural language description. Most existing methods rely on s…
Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models
Xuankun Rong, Wenke Huang, Bo Du +2
As large language models (LLMs) are increasingly used in decision support, it is important to understand whether their choices under uncertainty exhibit stable and interpretable be…
Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation
Xuetao Li, Wenke Huang, Mang Ye +4
Humanoid robot manipulation is a crucial research area for executing diverse human-level tasks, involving high-level semantic reasoning and low-level action generation. However, pr…
SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization
Xuankun Rong, Wenke Huang, Tingfeng Wang +3
Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new composition…
Multilingual Text-to-Image Person Retrieval via Bidirectional Relation Reasoning and Aligning
Min Cao, Xinyu Zhou, Ding Jiang +3
Text-to-image person retrieval (TIPR) aims to identify the target person using textual descriptions, facing challenge in modality heterogeneity. Prior works have attempted to addre…
MAPO: Mixed Advantage Policy Optimization
Wenke Huang, Quan Zhang, Yiyang Fang +11
Recent advances in reinforcement learning for foundation models, such as Group Relative Policy Optimization (GRPO), have significantly improved the performance of foundation models…
Backdoor Cleaning without External Guidance in MLLM Fine-tuning
Xuankun Rong, Wenke Huang, Jian Liang +5
Multimodal Large Language Models (MLLMs) are increasingly deployed in fine-tuning-as-a-service (FTaaS) settings, where user-submitted datasets adapt general-purpose models to downs…
ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation
Jian Liang, Wenke Huang, Xianda Guo +3
Low-Rank Adaptation (LoRA) is widely adopted for downstream fine-tuning of foundation models due to its efficiency and zero additional inference cost. Many real-world applications…
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
Wenke Huang, Jian Liang, Xianda Guo +14
Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. While MLLMs dem…
A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
Mang Ye, Xuankun Rong, Wenke Huang +3
With the rapid advancement of Large Vision-Language Models (LVLMs), ensuring their safety has emerged as a crucial area of research. This survey provides a comprehensive analysis o…
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
Wenke Huang, Jian Liang, Zekun Shi +6
Multimodal Large Language Model (MLLM) have demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets.…
EmoLLM: Multimodal Emotional Understanding Meets Large Language Models
Qu Yang, Mang Ye, Bo Du
Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks, but their ability to interpret subjective, emotionally nuan…
Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
Mang Ye, Wei Shen, Bo Du +3
Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared sa…
All in One Framework for Multimodal Re-identification in the Wild
He Li, Mang Ye, Ming Zhang +1
In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However, the challenge persists in developing a unified…
Transformer for Object Re-Identification: A Survey
Mang Ye, Shuoyi Chen, Chenyue Li +3
Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. For a prolonged period,…
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Wenke Huang, Mang Ye, Zekun Shi +4
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx…
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
Wenke Huang, Mang Ye, Zekun Shi +1
Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data. Model heteroge…